Speaker: Dr Smaranda Muresan (Columbia University, USA)

Abstract: Large-scale language models based on transformer architectures, such as GPT-3 or BERT, have advanced the state of the art in Natural Language Understanding and Generation. However, even though these models have shown impressive performance for a variety of tasks, they often struggle to model implicit and/or non-compositional meaning, such as figurative language and argumentative text. In this talk, I will present some of our recent work on text generation models for figurative language and argumentation. There are two main challenges we have to address to make progress in this space: 1) the need to model common sense and/or connotative knowledge required for these tasks; and 2) the lack of large training datasets. I will discuss our proposed theoretically-grounded knowledge-enhanced text generation models for figurative language such as metaphor and simile, as well as for enthymeme reconstruction and if time permits argument reframing. I will conclude by discussing opportunities and remaining challenges for incorporating knowledge in neural text generation systems.

Bio: Smaranda Muresan is a Research Scientist at the Data Science Institute at Columbia University and an Amazon Scholar. Before joining Columbia, she was a faculty member in the School of Communication and Information at Rutgers University where she co-founded the Laboratory for the Study of Applied Language Technologies and Society. At Rutgers, she was the recipient of the Distinguished Achievements in Research Award. Her research interests are in computational semantics and discourse, particularly figurative language understanding and generation, argument mining and generation, and fact-checking. Most recently, she has been interested in applying NLP to education and public health, as well as in building NLP technologies for low resource languages. She received best papers awards at SIGDIAL 2017 and ACL 2018 (short paper). She is currently serving as a Program Co-Chair for ACL 2022.

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